Join query processing using pruning index

ABSTRACT

A query directed at a table organized into a set of batch units is received. The query comprises a predicate for which values are unknown prior to runtime. A set of values for the predicate are determined based on the query. An index access plan is created based on the set of values. Based on the index access plan, the set of batch units are pruned using a pruning index associated with the table. The pruning index comprises a set of filters that index distinct values in each column of the table. The pruning of the set of batch units comprises identifying a subset of batch units to scan for data that satisfies the query. The subset of batch units of the table are scanned to identify data that satisfies the query.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.17/462,796, filed Aug. 31, 2021 entitled “PROCESSING TECHNIQUES FORQUERIES WHERE PREDICATE VALUES ARE UNKNOWN UNTIL RUNTIME,” which is acontinuation-in-part of U.S. Pat. No. 11,308,089 entitled “PRUNING INDEXMAINTENANCE,”; which is a continuation of U.S. Pat. No. 11,086,875,entitled “DATABASE QUERY PROCESSING USING A PRUNING INDEX,”; which is acontinuation of U.S. Pat. No. 10,942,925, entitled “DATABASE QUERYPROCESSING USING A PRUNING INDEX,”: which is a continuation of U.S. Pat.No. 10,769,150, entitled “PRUNING INDEXES TO ENHANCE DATABASE QUERYPROCESSING,” all of which are incorporated herein by reference in theirentireties.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, morespecifically, to using a pruning index to process queries where valuesfor one or more predicates are unknown until runtime.

BACKGROUND

When certain information is to be extracted from a database, a querystatement may be executed against the database data. A database systemprocesses the query and returns certain data according to one or moresearch conditions that indicate what information should be returned bythe query. The database system extracts specific data from the databaseand formats that data into a readable form. However, it can bechallenging to execute queries on a very large table because asignificant amount of time and computing resources are required to scanan entire table to identify data that satisfies the query.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageprovider system, in accordance with some embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments.

FIG. 4 is a conceptual diagram illustrating generation of an exampleblocked bloom filter, which may form part of a pruning index, inaccordance with some example embodiments.

FIG. 5 illustrates a portion of an example pruning index, in accordancewith some embodiments.

FIG. 6 is a conceptual diagram illustrating further details regardingthe creation of an example pruning index, in accordance with someembodiments.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex, in accordance with some embodiments.

FIGS. 8-12 are flow diagrams illustrating operations of thenetwork-based database system in performing a method for generating andusing a pruning index in processing a database query, in accordance withsome embodiments.

FIG. 13 is a flow diagram illustrating operations of the network-baseddatabase system in performing a method for processing a join query usinga pruning index, in accordance with some embodiments.

FIG. 14 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

As noted above, processing queries directed to very large tables ischallenging because a significant amount of time and computing resourcesare required to scan an entire table to identify data that satisfies thequery. Therefore, it can be desirable to execute a query withoutscanning the entire table. Aspects of the present disclosure address theabove and other challenges in processing queries on large tables bycreating a pruning index that may be used to construct a reduced scanset for processing a query. More specifically, a large source table maybe organized into a set of batch units such as micro-partitions, and apruning index can be created for the source table to be used inidentifying a subset of the batch units to scan to identify data thatsatisfies the query.

It is common for data to be stored by database systems insemi-structured formats, which can store objects of any kind such asnumbers, strings, timestamps, or the like. Accordingly, the pruningindexes described herein are configured to support primitive data types(e.g., STRING, NUMBER, or the like) as well as such semi-structured andcomplex (e.g., ARRAY and OBJECT) data types.

Consistent with some embodiments, a network-based database systemgenerates a pruning index for a source table and uses the pruning indexto prune micro-partitions of the source table when processing queriesdirected to the source table. The pruning index includes a probabilisticdata structure that stores fingerprints for all searchable values in asource table. The fingerprints are based on hashes computed based onsearchable values in the source table. To support semi-structured datatype values, hashes can be computed over indexing transformations ofsearchable values. That is, for each semi-structured data type value,one or more indexing transformations are generated and the fingerprintsare generated based on hashes computed over the one or more indexingtransformations. An indexing transformation is generated by converting asemi-structured data value to a primitive data type. To support partialmatching queries, fingerprints can be generated by computing a hash overa set of N-grams generated based on a searchable value, in someembodiments.

In generating a pruning index, the network-based database system usesthe fingerprints to generate a filter for each micro-partition of thesource table that indexes distinct values (or distinct N-grams ofsearchable values) in each column of the micro-partition of the sourcetable. The filter may, for example, comprise a blocked bloom filter, abloom filter, a hash filter, or a cuckoo filter.

For a given query, the pruning index can be used to quickly disqualifymicro-partitions that are certain to not include data that satisfies thequery. When a query is received, rather than scanning the entire sourcetable to identify matching data, the network-based database systemprobes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table, and only the reduced scan set of micro-partitions isscanned when executing the query.

The database system can use a pruning index that supports bothstructured and semi-structured data types to prune a scan set forqueries with equality predicates (e.g., “=”), queries with patternmatching predicates (e.g., LIKE, ILIKE, CONTAINS, STARTSWITH, ENDSWITH,etc.), and queries where values for predicates are unknown prior toruntime. As discussed herein, a “predicate” can comprise an expression(e.g., a SQL expression) that evaluates a search condition that iseither TRUE, FALSE, or UNKNOWN. For a given equality predicate, thedatabase system uses the pruning index to identify a subset ofmicro-partitions to scan for data that matches an entire string or othersearchable value. For a given pattern matching predicate, the databasesystem uses the pruning index to identify a set of micro-partitions toscan for data that matches a specified search pattern, which can includeone or more partial strings and one or more wildcards (e.g., “% ” or“_”) used to represent wildcard character positions in the pattern(e.g., character positions whose underlying value is unconstrained bythe query).

For certain types of queries, values for a predicate may only becomeavailable at runtime (i.e., after query compilation is complete, duringthe query execution). Examples of these types of queries include querieswith join clauses (also referred to simply as “join queries”), queriesthat include sub-queries. The join clause is a means for combiningcolumns from one or more tables by using values common to each of theone or more tables. In general, a join is an operation in queryprocessing that determines rows in two inputs that “match” with respectto some of their attributes, which are referred to as join keys. Joinoperations are typically very time-consuming operations during queryexecution.

A hash join is an example of a join algorithm that may be used in theimplementation of a relational database management system. Various formsof hash joins are commonly used in database systems to compute theresult of a join. Hash joins build one or more multiple hash tables withrows of one of the inputs (typically the smaller input) referred to asthe “build side” input. The rows are probed from the other input(typically the larger input) referred to as the “probe side” input andinto the hash tables.

Join pruning is a conventional pruning technique used for processingjoin queries. With join pruning, a data structure (e.g., a range bloomfilter) representing a synopsis of values from the build side table issent to a probe-side scan operator, and micro-partitions are prunedusing per-partition metadata that includes the minimum and maximum valueper column and micro-partition before the micro-partitions are scanned.By comparing this min-max span to the values that have been sent over tothe scan operator, the system can identify and disregardmicro-partitions that cannot contain matching tuples because the min-maxspan does not contain the values being searched for. However,false-positives are a significant problem with join pruning. That is,join pruning techniques can fail to prune micro-partitions that do notcontain matching tuples values because: (1) only the minimum and maximumvalues are considered without considering information about the presenceor absence of tuples with values within the min-max span; and (2) rangebloom filters are compared to the metadata rather than individualvalues. Generally, join pruning only works well if the probe side tableis more or less clustered by the join column. If it is not, the min-maxspan of every micro-partition will approximately encompass the wholedata domain, thereby significantly reducing the number ofmicro-partitions that can be pruned because the values being searchedwill most likely fall within the span. Accordingly, it would beadvantageous to instead utilize pruning indexes for join queries and anyother queries for which values for predicates are unknown prior toruntime.

To utilize a pruning index for these types of queries, the networkdatabase system collects a set of values for a query predicate atruntime and creates an index access plan based on the set of values. Thesystem can use a pruning index associated with a table to which thequery is directed to identify the reduced scan set of micro-partitionsbased on the index access plan, and only the reduced scan set is scannedfor data that satisfies the predicate.

In the specific example of a join query to combine rows from two tables,the system can collect the set of values for the query predicate duringa join build phase in which a hash table is created in which rows from afirst table (also referred to as the “build side table”) are storedusing the join attribute(s) as the hash key. The collected values areused in conjunction with a pruning index associated with a second table(also referred to as the “probe side table”) to identify the reducedscan set micro-partitions. The system performs the join probe phaseusing the reduced scan set of micro-partitions.

Consistent with some embodiments, use of a pruning index in processing agiven query can be conditional and whether to use the pruning index canbe decided dynamically at runtime. That is, a cost function can be usedto evaluate use of a pruning index for a given query, and if the cost istoo high (e.g., above a predetermined cost value threshold), the pruningindex is not used. As an example, use of a pruning index can be based onthe number of micro-partitions in the table to which a pruning index isassociated. As another example, use of a pruning index can be based onthe number of values for a given predicate. Accordingly, prior tocreating the index access plan, the system may determine whether thenumber of micro-partitions in the table satisfies a thresholdconstraint, whether the number of values in the set of values satisfiesa threshold constraint, or both. If such a threshold constraint is notsatisfied, the system does not use the pruning index for pruning and mayutilize an alternative pruning mechanism such as join pruning.

Use of pruning indexes at runtime in processing queries in whichpredicate values are unknown until runtime can improve the speed atwhich the queries are executed while also reducing costs as it pertainsto use of computational resources. Moreover, this approach allows formore flexibility. For example, this approach would allow work to beredistributed between worker nodes and thus reduce idle times caused bydata skew. Generally, techniques for runtime use of pruning indexesallow the benefits of pruning indexes to be extended to a wider set ofqueries.

By using a pruning index to prune the set of micro-partitions to scan inexecuting a query, the database system accelerates the execution ofpoint queries on large tables when compared to conventionalmethodologies. Using a pruning index in this manner also guarantees aconstant overhead for every searchable value on the table. Additionalbenefits of pruning index utilization include, but are not limited to,an ability to support multiple predicate types, an ability to quicklycompute the number of distinct values in a table, and the ability tosupport join pruning.

In addition, by utilizing indexing transformations when building thepruning index, query predicates on semi-structured fields can besupported. Contrary to conventional approaches, this approach does notrequire manual selection of semi-structured data fields to be indexed.Further, unlike conventional approaches, this approach does not use anyadditional storage or concepts such as virtual columns or generatedcolumns to store the fields to be indexed. Also, the pruning indexesdescribed herein support indexing and matching against predicatesregardless of how nested or evolving the structure of the input data is.Moreover, this approach does not enforce any data type restrictions onthe semi-structured data fields and the values in the predicate.Finally, the generation of the pruning index involves cast-sensitiveindexing of individual semi-structured data type fields, meaning thateach input record can be attempted to be converted to relevant datatypes to match with casting behavior of semi-structured data typecolumns.

As discussed herein, a “micro-partition” is a batch unit, and eachmicro-partition has contiguous units of storage. By way of example, eachmicro-partition may contain between 50 MB and 500 MB of uncompresseddata (note that the actual size in storage may be smaller because datamay be stored compressed). Groups of rows in tables may be mapped intoindividual micro-partitions organized in a columnar fashion. This sizeand structure allow for extremely granular selection of themicro-partitions to be scanned, which can be comprised of millions, oreven hundreds of millions, of micro-partitions. This granular selectionprocess for micro-partitions to be scanned is referred to herein as“pruning.” Pruning involves using metadata to determine which portionsof a table, including which micro-partitions or micro-partitiongroupings in the table, are not pertinent to a query, and then avoidingthose non-pertinent micro-partitions when responding to the query andscanning only the pertinent micro-partitions to respond to the query.Metadata may be automatically gathered about all rows stored in amicro-partition, including: the range of values for each of the columnsin the micro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded. However, itshould be appreciated that this disclosure of the micro-partition isexemplary only and should be considered non-limiting. It should beappreciated that the micro-partition may include other database storagedevices without departing from the scope of the disclosure.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system 102 in communication with a storage platform 104, inaccordance with some embodiments of the present disclosure. To avoidobscuring the inventive subject matter with unnecessary detail, variousfunctional components that are not germane to conveying an understandingof the inventive subject matter have been omitted from FIG. 1. However,a skilled artisan will readily recognize that various additionalfunctional components may be included as part of the computingenvironment 100 to facilitate additional functionality that is notspecifically described herein.

As shown, the computing environment 100 comprises the database system102 and a storage platform 104 (e.g., AWS®, Microsoft Azure BlobStorage®, or Google Cloud Storage®). The database system 102 is used forreporting and analysis of integrated data from one or more disparatesources including storage devices 106-1 to 106-N within the storageplatform 104. The storage platform 104 comprises a plurality ofcomputing machines and provides on-demand computer system resources suchas data storage and computing power to the database system 102.

The database system 102 comprises a compute service manager 108, anexecution platform 110, and a database 114. The database system 102hosts and provides data reporting and analysis services to multipleclient accounts. Administrative users can create and manage identities(e.g., users, roles, and groups) and use permissions to allow or denyaccess to the identities to resources and services.

The compute service manager 108 coordinates and manages operations ofthe database system 102. The compute service manager 108 also performsquery optimization and compilation as well as managing clusters ofcompute services that provide compute resources (also referred to as“virtual warehouses”). The compute service manager 108 can support anynumber of client accounts such as end users providing data storage andretrieval requests, system administrators managing the systems andmethods described herein, and other components/devices that interactwith compute service manager 108.

The compute service manager 108 is also in communication with a userdevice 112. The user device 112 corresponds to a user of one of themultiple client accounts supported by the database system 102. In someembodiments, the compute service manager 108 does not receive any directcommunications from the user device 112 and only receives communicationsconcerning jobs from a queue within the database system 102.

The compute service manager 108 is also coupled to database 114, whichis associated with the data stored in the computing environment 100. Thedatabase 114 stores data pertaining to various functions and aspectsassociated with the database system 102 and its users. In someembodiments, the database 114 includes a summary of data stored inremote data storage systems as well as data available from a localcache. Additionally, the database 114 may include information regardinghow data is organized in remote data storage systems (e.g., the storageplatform 104) and the local caches. The database 114 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

For example, the database 114 can include one or more pruning indexes.The compute service manager 108 may generate a pruning index for eachsource table accessed from the storage platform 104 and use a pruningindex to prune the set of micro-partitions of a source table to scan fordata in executing a query. That is, given a query directed at a sourcetable organized into a set of micro-partitions, the compute servicemanager 108 can access a pruning index from the database 114 and use thepruning index to identify a reduced set of micro-partitions to scan inexecuting the query. The set of micro-partitions to scan in executing aquery may be referred to herein as a “scan set.”

In some embodiments, the compute service manager 108 may determine thata job should be performed based on data from the database 114. In suchembodiments, the compute service manager 108 may scan the data anddetermine that a job should be performed to improve data organization ordatabase performance. For example, the compute service manager 108 maydetermine that a new version of a source table has been generated andthe pruning index has not been refreshed to reflect the new version ofthe source table. The database 114 may include a transactional changetracking stream indicating when the new version of the source table wasgenerated and when the pruning index was last refreshed. Based on thattransaction stream, the compute service manager 108 may determine that ajob should be performed. In some embodiments, the compute servicemanager 108 determines that a job should be performed based on a triggerevent and stores the job in a queue until the compute service manager108 is ready to schedule and manage the execution of the job. In anembodiment of the disclosure, the compute service manager 108 determineswhether a table or pruning index needs to be reclustered based on one ormore DML, commands being performed, wherein one or more of the DMLcommands constitute the trigger event.

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to the storage platform 104. The storage platform 104comprises multiple data storage devices 106-1 to 106-N. In someembodiments, the data storage devices 106-1 to 106-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, the data storage devices 106-1 to 106-N may be part of a publiccloud infrastructure or a private cloud infrastructure. The data storagedevices 106-1 to 106-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3™ storage systems or any otherdata storage technology. Additionally, the storage platform 104 mayinclude distributed file systems (e.g., Hadoop Distributed File Systems(HDFS)), object storage systems, and the like.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete micro-partition files using a least recently used (LRU) policyand implement an out of memory (OOM) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, the data storage devices 106-1 to 106-N aredecoupled from the computing resources associated with the executionplatform 110. This architecture supports dynamic changes to the databasesystem 102 based on the changing data storage/retrieval needs as well asthe changing needs of the users and systems. The support of dynamicchanges allows the database system 102 to scale quickly in response tochanging demands on the systems and components within the databasesystem 102. The decoupling of the computing resources from the datastorage devices supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources.

The compute service manager 108, database 114, execution platform 110,and storage platform 104 are shown in FIG. 1 as individual discretecomponents. However, each of the compute service manager 108, database114, execution platform 110, and storage platform 104 may be implementedas a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations). Additionally, eachof the compute service manager 108, database 114, execution platform110, and storage platform 104 can be scaled up or down (independently ofone another) depending on changes to the requests received and thechanging needs of the database system 102. Thus, in the describedembodiments, the database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the database system 102 processes multiplejobs determined by the compute service manager 108. These jobs arescheduled and managed by the compute service manager 108 to determinewhen and how to execute the job. For example, the compute servicemanager 108 may divide the job into multiple discrete tasks and maydetermine what data is needed to execute each of the multiple discretetasks. The compute service manager 108 may assign each of the multiplediscrete tasks to one or more nodes of the execution platform 110 toprocess the task. The compute service manager 108 may determine whatdata is needed to process a task and further determine which nodeswithin the execution platform 110 are best suited to process the task.Some nodes may have already cached the data needed to process the taskand, therefore, be a good candidate for processing the task. Metadatastored in the database 114 assists the compute service manager 108 indetermining which nodes in the execution platform 110 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 110 process the task using datacached by the nodes and, if necessary, data retrieved from the storageplatform 104. It is desirable to retrieve as much data as possible fromcaches within the execution platform 110 because the retrieval speed istypically much faster than retrieving data from the storage platform104.

As shown in FIG. 1, the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices106-1 to 106-N in the storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 106-1 to 106-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, the compute service manager 108 includesan access manager 202 and a key manager 204 coupled to a data storagedevice 206. Access manager 202 handles authentication and authorizationtasks for the systems described herein. Key manager 204 manages storageand authentication of keys used during authentication and authorizationtasks. For example, access manager 202 and key manager 204 manage thekeys used to access data stored in remote storage devices (e.g., datastorage devices in storage platform 104). As used herein, the remotestorage devices may also be referred to as “persistent storage devices”or “shared storage devices.”

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata necessary to process a received query (e.g., a data storage requestor data retrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 218 determines a priority for internaljobs that are scheduled by the compute service manager 108 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 110. In some embodiments, the job scheduler andcoordinator 218 identifies or assigns particular nodes in the executionplatform 110 to process particular tasks. A virtual warehouse manager220 manages the operation of multiple virtual warehouses implemented inthe execution platform 110. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local caches(e.g., the caches in execution platform 110). The configuration andmetadata manager 222 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 224 overseesprocesses performed by the compute service manager 108 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 110. The monitor and workloadanalyzer 224 also redistributes tasks, as needed, based on changingworkloads throughout the database system 102 and may furtherredistribute tasks based on a user (e.g., “external”) query workloadthat may also be processed by the execution platform 110. Theconfiguration and metadata manager 222 and the monitor and workloadanalyzer 224 are coupled to a data storage device 226. Data storagedevice 226 in FIG. 2 represents any data storage device within thedatabase system 102. For example, data storage device 226 may representcaches in execution platform 110, storage devices in storage platform104, or any other storage device.

As shown, the compute service manager 108 further includes a pruningindex generator 228. The pruning index generator 228 is responsible forgenerating pruning indexes to be used in pruning scan sets for queriesdirected to tables stored in the storage platform 104. Each pruningindex comprises a set of filters (e.g., blocked bloom filters, bloomfilters, hash filter, or cuckoo filters) that encode an existence ofunique values in each column of a source table. The pruning indexgenerator 228 generates a filter for each micro-partition of a sourcetable and each filter indicates whether data matching a query ispotentially stored on a particular micro-partition of the source table.Further details regarding the generation of pruning indexes arediscussed below.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each includes a data cache and aprocessor. The virtual warehouses can execute multiple tasks in parallelby using the multiple execution nodes. As discussed herein, theexecution platform 110 can add new virtual warehouses and drop existingvirtual warehouses in real-time based on the current processing needs ofthe systems and users. This flexibility allows the execution platform110 to quickly deploy large amounts of computing resources when neededwithout being forced to continue paying for those computing resourceswhen they are no longer needed. All virtual warehouses can access datafrom any data storage device (e.g., any storage device in storageplatform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 106-1 to 106-N shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 106-1 to106-N and, instead, can access data from any of the data storage devices106-1 to 106-N within the storage platform 104. Similarly, each of theexecution nodes shown in FIG. 3 can access data from any of the datastorage devices 106-1 to 106-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse N includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternate embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in storageplatform 104. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the storage platform 104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a conceptual diagram illustrating generation of a filter 400,which forms part of a pruning index generated by the database system 102based on a source table 402, in accordance with some exampleembodiments. As shown, the source table 402 is organized into multiplemicro-partitions and each micro-partition comprises multiple columns inwhich values are stored.

In generating a pruning index, the compute service manager 108 generatesa filter for each micro-partition of the source table 402, an example ofwhich is illustrated in FIG. 4 as blocked bloom filter 400. Blockedbloom filter 400 comprises multiple bloom filters and encodes theexistence of distinct values present in each column of the correspondingmicro-partition. When a query is received, rather than scanning theentire source table 402 to evaluate the query, the database system 102probes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table 402.

As shown, the blocked bloom filter 400 is decomposed into N bloomfilters stored as individual columns of the pruning index to leveragecolumnar scans. In generating the blocked bloom filter 400 for aparticular micro-partition of the source table 402, values of storedvalues or preprocessed variants thereof are transformed into bitpositions in the bloom filters. For example, a set of fingerprints(e.g., hash values) can be generated from stored values (or N-gramsgenerated from stored values) in each column of the micro-partition andthe set of fingerprints may be used to set bits in the bloom filters.Each line of the blocked bloom filter 400 is encoded and stored as asingle row in the pruning index. Each bloom filter 400 is represented inthe pruning index as a two-dimensional array indexed by the fingerprintsfor the stored column values.

FIG. 5 illustrates a portion of an example pruning index 500, inaccordance with some embodiments of the present disclosure. The examplepruning index 500 is organized into a plurality of rows and columns. Thecolumns of the pruning index 500 comprise a partition number 502 tostore a partition identifier and a blocked bloom filter 504 (e.g., theblocked bloom filter 400) that is decomposed into multiple numericcolumns; each column in the blocked bloom filter 504 represents a bloomfilter. To avoid obscuring the inventive subject matter with unnecessarydetail, various additional columns that are not germane to conveying anunderstanding of the inventive subject matter may have been omitted fromthe example pruning index 500 in FIG. 5.

FIG. 6 is a conceptual diagram illustrating creation of an examplepruning index, in accordance with some embodiments. The creation of afilter (e.g., a blocked bloom filter) is performed by a specializedoperator within the compute service manager 108 that computes the set ofrows of the pruning index. This operator obtains all the columns of aparticular micro-partition of a source table and populates the filterfor that micro-partition.

If the total number of distinct values (or distinct N-grams of storedvalues) in the source table is unknown, the compute service manager 108allocates a maximum number of levels to the pruning index, populateseach filter, and then applies a consolidation phase to merge thedifferent filters in a final representation of the pruning index. Thememory allocated to compute this information per micro-partition isconstant. In the example illustrated in FIG. 6, the memory allocated tocompute this information is a two-dimensional array of unsignedintegers. The first dimension is indexed by the level (maximum number oflevels) and the second dimension is indexed by the number of bloomfilters. Since each micro-partition is processed by a single thread, thetotal memory is bounded by the number of threads (e.g., 8) and themaximum level of levels.

As shown in FIG. 6, at each micro-partition boundary, the computeservice manager 108 combines blocks based on a target bloom filterdensity. For example, the compute service manager 108 may combine blockssuch that the bloom filter density is no more than half. Since thedomain of fingerprints (e.g., hashed values) is uniform, this can bedone incrementally or globally based on the observed number of distinctvalues computed above.

If the number of distinct values is known, the compute service manager108 determines the number of levels for the pruning index by dividingthe maximum number of distinct values (or distinct N-grams) by thenumber of distinct values (or distinct N-grams) per level. To combinetwo levels, the compute service manager 108 performs a logical OR on allthe integers representing the filter.

For performance reasons, the filter functions (create and check) cancombine two hash functions (e.g., two 32-bit hash functions). Both thehash function computation and the filter derivation need to be identicalon both the execution platform 110 and compute service manager 108 toallow for pruning in compute service manager 108 and in the scan setinitialization in the execution platform 110.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex based on changes to a source table, in accordance with someembodiments. As shown, at 700, a change is made to a source table (e.g.,addition of one or more rows or columns). The change to the source tabletriggers generation of additional rows in the pruning index for eachchanged or new micro-partition of the source table, at 702. At a regularinterval, the newly produced rows in the pruning index are reclustered,at 704.

The compute service manager 108 uses a deterministic selection algorithmas part of clustering the prune index. The processing of eachmicro-partition in the source table creates a bounded (and mostlyconstant) number of rows based on the number of distinct values (orN-grams of stored values) in the source micro-partition. Byconstruction, those rows are known to be unique and the index domain isnon-overlapping for that partition and fully overlapping with alreadyclustered index rows. To minimize the cost of clustering, the computeservice manager 108 delays reclustering of rows until a threshold numberof rows have been produced to create constant partitions.

Although the pruning index is described in some embodiments as beingimplemented specifically with blocked bloom filters, it shall beappreciated that the pruning index is not limited to blocked bloomfilters, and in other embodiments, the pruning index may be implementedusing other filters such as bloom filters, hash filters, or cuckoofilters.

FIGS. 8-12 are flow diagrams illustrating operations of the databasesystem 102 in performing a method 800 for generating and using a pruningindex in processing a database query, in accordance with someembodiments of the present disclosure. The method 800 may be embodied incomputer-readable instructions for execution by one or more hardwarecomponents (e.g., one or more processors) such that the operations ofthe method 800 may be performed by components of database system 102.Accordingly, the method 800 is described below, by way of example withreference thereto. However, it shall be appreciated that the method 800may be deployed on various other hardware configurations and is notintended to be limited to deployment within the database system 102.

Depending on the embodiment, an operation of the method 800 may berepeated in different ways or involve intervening operations not shown.Though the operations of the method 800 may be depicted and described ina certain order, the order in which the operations are performed mayvary among embodiments, including performing certain operations inparallel or performing sets of operations in separate processes. Forexample, although the use and generation of the pruning index aredescribed and illustrated together as part of the method 800, it shallbe appreciated that the use and generation of the pruning index may beperformed as separate processes, consistent with some embodiments.

At operation 805, the compute service manager 108 accesses a sourcetable that is organized into a plurality of micro-partitions. The sourcetable comprises a plurality of cells organized into rows and columns anda data value is included in each cell.

At operation 810, the compute service manager 108 generates a pruningindex based on the source table. The pruning index comprises a set offilters (e.g., a set of blocked bloom filters) that index distinctvalues (or distinct N-grams of stored values) in each column of eachmicro-partition of the source table. A filter is generated for eachmicro-partition in the source table and each filter is decomposed intomultiple numeric columns (e.g., 32 numeric columns) to enable integercomparisons. Consistent with some embodiments, the pruning indexcomprises a plurality of rows and each row comprises at least amicro-partition identifier and a set of bloom filters. Consistent withsome embodiments, the compute service manager 108 generates the pruningindex in an offline process before receiving a query.

At operation 815, the compute service manager 108 stores the pruningindex in a database with an association with the source table such thatthe pruning index can be retrieved upon receiving a query directed atthe source table.

At operation 820, the compute service manager 108 receives a querydirected at the source table. The query can comprise an equalitypredicate (e.g., “=”), a pattern matching predicate (e.g., LIKE, ILIKE,CONTAINS, STARTSWITH, or ENDSWITH), or an in-list predicate (e.g.,‘column IN (value1, value2)’ is equal to ‘column=value1 ORcolumn=value2’). In instances in which the query includes a patternmatching predicate, the query specifies a search pattern for whichmatching stored data in the source table is to be identified. A querypredicate can be directed to primitive data types (e.g., STRING, NUMBER,or the like), complex data types (e.g., ARRAY or OBJECT),semi-structured data types (e.g., JSON, XML, Parquet, and ORC), orcombinations thereof. In some instances, values for one or more querypredicates may be unknown prior to runtime. For example, the query caninclude a join clause or a subquery.

At operation 825, the compute service manager 108 accesses the pruningindex associated with the source table based on the query being directedat the source table. For example, the database 114 may store informationdescribing associations between tables and pruning indexes.

At operation 830, the compute service manager 108 uses the pruning indexto prune the set of micro-partitions of the source table to be scannedfor data that satisfies the query (e.g., a data value that satisfies aquery predicate or data that matches the search pattern). That is, thecompute service manager 108 uses the pruning index to identify a reducedscan set comprising only a subset of the micro-partitions of the sourcetable. The reduced scan set includes one or more micro-partitions inwhich data that satisfies the query is potentially stored. The subset ofmicro-partitions of the source table include micro-partitions determinedto potentially include data that satisfies the query based on the set ofbloom filters in the pruning index.

At operation 835, the execution platform 110 processes the query. Inprocessing the query, the execution platform 110 scans the subset ofmicro-partitions of the reduced scan set while foregoing a scan of theremaining micro-partitions. In this way, the execution platform 110searches only micro-partitions where matching data is potentially storedwhile foregoing an expenditure of additional time and resources to alsosearch the remaining micro-partitions for which it is known, based onthe pruning index, that matching data is not stored.

Consistent with some embodiments, rather than providing a reduced scanset with micro-partitions of the source table to scan for data, thecompute service manager 108 may instead identify and compile a set ofnon-matching micro-partitions. The compute service manager 108 or theexecution platform 110 may remove micro-partitions from the scan setbased on the set of non-matching micro-partitions.

The processing of the query can include executing a query plan compiledby the compute service manager 108. In some embodiments (e.g., wherevalues for query predicates are known prior to query runtime), the queryplan may indicate the reduced scan set to scan. In some embodiments,values for one or more query predicates are unknown prior to queryruntime, and only upon determining these values at runtime can thepruning index be used to prune the scan set. Consistent with theseembodiments, the pruning of the scan set (operation 830) can also beperformed at runtime as part of executing the query plan compiled by thecompute service manager 108. Further details regarding processing ofqueries where values for one or more query predicates are unknown priorto runtime are discussed below.

As shown in FIG. 9, the method 800 may, in some embodiments, furtherinclude operations 905 and 910. Consistent with these embodiments, theoperations 905 and 910 may be performed as part of the operation 810where the compute service manager 108 generates the pruning index. Theoperations 905 and 910 are described below in reference to a singlemicro-partition of the source table simply for ease of explanation.However, it shall be appreciated that in generating the pruning index,the compute service manager 108 generates a filter for eachmicro-partition of the source table, and thus the operations 905 and 910may be performed for each micro-partition of the source table.

At operation 905, the compute service manager 108 generates a filter fora micro-partition of the source table. For example, the compute servicemanager 108 may generate a blocked bloom filter for the micro-partitionthat indexes distinct values (or distinct N-grams of values) in eachcolumn of the micro-partition of the source table. The generating of thefilter can include generating a set of fingerprints for each searchabledata value in the micro-partition.

Given that objects in semi-structured data type columns can be stored aspotentially multiple different data types by the network-based databasesystem 102 (referred to herein as “stored data types”), the computeservice manager 108 can, in some embodiments, generate fingerprints fora given object in a semi-structured column of the source table based onone or more data type transformations generated for the object, as willbe discussed in further detail below. A data type transformation can begenerated by converting a data object into a stored data type, forexample, using an SQL Cast Function (also referred to simply as a“cast”). By generating fingerprints in this matter, the compute servicemanager 108 can support indexing of semi-structured data type objectsincluded in the source table.

In some embodiments, for a given data value in the micro-partition, thecompute service manager 108 can generate the set of fingerprints basedon a set of N-grams generated for the data value. The set of N-grams canbe generated based on the data value or one or more preprocessedvariants of the data value. The compute service manager 108 can generatea fingerprint based on a hash that is computed of an N-gram.

In computing the hash, the compute service manager 108 may utilize arolling hash function or other known hashing scheme that allowsindividual characters to be added or removed from a window ofcharacters. An example hash function used by the compute service manager108 is the XxHash( ) function, although other known hash functions canbe utilized. Each generated fingerprint is used to populate a cell inthe filter.

At operation 910, which is optional in some embodiments, the computeservice manager 108 merges one or more rows of the filter. The computeservice manager 108 can merge rows by performing a logical OR operation.The compute service manager 108 may merge rows of the filter until adensity threshold is reached, where the density refers to the ratio of1's and 0's in a row. The density threshold may be based on a targetfalse positive rate.

In some instances, values for one or more predicates for the query(received at operation 820) may be unknown until runtime (e.g., untilexecution of the query). For example, the query may include a joinclause to combine rows from two or more tables. The following is anexample of a typical join query:

SELECT ... FROM fact JOIN dimension ON (fact.dimension_id_foreign_key =dimension.id) WHERE dimension.column1 = ‘xyz’;

Because of the additional “dimension.column1=‘xyz’” predicate, not allvalues of the “dimension.id” domain are present in the set of tuplesthat form the build side table of this join. Only tuples of the facttable whose “fact.dimension_id_foreign_key” column equals one of thesevalues have to be scanned to answer the query at hand.

Another example of queries in which the values for predicates becomeavailable only at runtime are subqueries, an example of which is asfollows:

SELECT table.column2 FROM table WHERE table.column1 = (SELECTAVG(column3) FROM table2);Here, the value that “table.column1” has to be equal to is only known atruntime, after the subquery has been executed.

To handle such queries, the method 800 can, in some embodiments, includeoperations 1005, 1010, 1011, 1012, 1015, 1020, and 1025 as shown in FIG.10. With reference to FIG. 10, the operations 1005, 1010, 1011, 1012,and 1015 can be performed subsequent to operation 820 where the computeservice manager 108 receives a query in which values for one or morepredicates are unknown until runtime (e.g., a query with a joinstatement or subqueries). Consistent with these embodiments, any one ormore of the operations 1005, 1010, 1011, 1012, and 1015 can be performedsubsequent to compilation of the query. For example, any one or more ofthe operations 1005, 1010, 1011, 1012, and 1015 can be performed atruntime. In addition, any one of the operations 825, 830, and 835 can beperformed at runtime, consistent with these embodiments.

At operation 1005, the execution platform 110 determines a set of valuesfor a query predicate that were unknown prior to runtime. In an example,the query includes a join clause to combine rows for at least twotables, and the execution platform 110 collects a set of values for thequery predicate in performing a join build phase in which the computeservice manager 108 builds a hash table where rows from a first table(the build side table) are stored using the join attribute(s) as thehash key. In general, join predicates can be converted into either anequality predicate or an in-list predicate, depending on how many valuesare in the build side. In another example, the query includes asubquery, which is to be executed before the values to match against areknown. In this example, determining the set of values includes executinga subquery.

At operation 1010, the execution platform 110 evaluates whether to usethe pruning index to prune the scan set. In doing so, the executionplatform 110 uses a cost function to evaluate the use of the pruningindex. If the cost associated with using the pruning index is too high(e.g., the cost exceeds a predetermined cost threshold), the executionplatform 110 does not use the pruning index. Otherwise, the executionplatform 110 prunes the scan set with the pruning index.

As shown, in some embodiments, the evaluation performed at operation1010 can include operations 1011 and 1012. At operation 1011, theexecution platform 110 determines whether the number of values in theset of values satisfies a threshold constraint. In determining whetherthe number of values satisfies the threshold constraint, the executionplatform 110 may compare the number of values in the set of values to athreshold number, and if the number of values in the set does not exceedthe threshold number, the execution platform 110 determines the numberof values in the set satisfies the threshold constraint. Otherwise, thethreshold constraint is not satisfied. If the threshold constraint isnot satisfied, the method 800 moves to operation 1025 where analternative pruning mechanism is used to prune the scan set rather thanthe pruning index. For example, the execution platform 110 can performconventional join pruning techniques.

If the number of values in the set of values satisfies the thresholdconstraint, the method 800 advances to operation 1012 where theexecution platform 110 determines whether the number of micro-partitionsin the table (to which the query is directed) satisfies a thresholdconstraint. In determining whether the number of micro-partitionssatisfies the threshold constraint, the execution platform 110 maycompare the number of micro-partitions in the table to a thresholdnumber of micro-partitions, and if the number of micro-partitions doesnot exceed the threshold number, the execution platform 110 determinesthe number of micro-partitions in the set satisfies the thresholdconstraint. Otherwise, the threshold constraint is not satisfied. If thethreshold constraint is not satisfied, the method 800 moves to operation1025 where an alternative pruning mechanism is used to prune the scanset rather than the pruning index.

If the execution platform 110 decides to use the pruning index based onthe evaluation using the cost function (e.g., if the number of values inthe set of values satisfies the threshold constraint and/or if thenumber of micro-partitions in the table satisfies the thresholdconstraint), the execution platform 110 generates an index access planbased on the set of values, at operation 1015. The index access planspecifies the set of values to compare against the pruning index todetermine the reduced scan set. In some embodiments, the executionplatform 110 may, as part of generating the access plan, generate a datastructure based on the set of values. As non-limiting example, the datastructure can be a list of the values.

Based on the index access plan, the execution platform 110 accesses thepruning index associated with the source table (operation 825) and usesthe pruning index to prune the scan set at runtime (operation 830). Asshown in FIG. 10, in some embodiments, the operation 1020 can beperformed as part of the operation 830 where the execution platform 110prunes the scan set using the pruning index. At operation 1020, theexecution platform 110 uses the pruning index to identify a subset ofmicro-partitions of the table to scan based on the index access plan.For example, the execution platform 110 may generate one or morefingerprints for each value in the set of values (e.g., by computing ahash over the values) and compare the computed fingerprints to thepruning index to identify a reduced scan set comprising only a subset ofthe micro-partitions of the table in which data that satisfies the queryis potentially stored. That is, the execution platform 110 identifiesone or more values (e.g., fingerprints) in the pruning index that matchat least one fingerprint in the set of fingerprints. The subset ofmicro-partitions of the source table include micro-partitions determinedto potentially include data that satisfies the query.

In the example in which the query includes a join clause, the processingof the query includes performing a join probe phase in which executionnodes of the execution platform 110 read rows from a second table (theprobe side table) and probe the hash table for a matching row using ajoin attribute as the lookup key. For each match that is identified, ajoined row is returned. In this example, the probe side tablecorresponds to the pruning index accessed at operation 825. Inperforming the join probe phase, only the reduced scan set from theprobe side table is used to probe the hash table. That is, only rowsfrom micro-partitions in the reduced scan set are used to probe the hashtable to identify matching rows.

In some instances, a table can include one or more columns of data of asemi-structured data type used to store objects of any kind, such asprimitive data types like numbers, strings, binary data, date, time, andtimestamp values, as well as compound data types such as objects andarrays that store a nested structure inside. Accordingly, it isimportant that pruning indexes generated by the database system 102 alsosupport query predicates on semi-structured data types in addition topredicates on primitive data-type fields. As non-limiting examples, apruning index can be generated to support the following types ofpredicates:

• ... where <path_to_semi-structured_data_type_field> = <constant>; • ... where <path_to_semi-structured_data_type_(——)field>::<cast_to_type>= <constant> •  ... where <_semi-structured_data_type_column> =<constant>; • ... where <path_to_(——)semi-structured_data_type_field>like ‘%pattern%’; •  ... where <_semi-structured_data_type_column> like‘%pattern%’; • ARRAY predicates: array_contains(<value>);arrays_overlap(<array1>, <array2>)

Extending the pruning index to support such semi-structured predicatetypes can present a number of challenges. For example, semi-structureddata type schemas such as JSON schemas can be highly nested (e.g., afield can contain an ARRAY object, which in turn holds values ofheterogeneous types, including other complex types such as OBJECT orARRAY). As another example, semi-structured data can evolve in time(e.g., new fields can be added or existing fields can be removed). Asanother example, the data type for the same field in one row can bedifferent from the one in another row (e.g., an ID field can berepresented as a NUMBER and STRING in different rows). As yet anotherexample of the challenges posed by semi-structured data types, a singlevalue might correspond to multiple data types (e.g., a STRING value cancontain a valid DATE, TIME, TIMESTAMP, NUMBER, etc. data types). Instill another example, the same value might match against severalsemi-structured data types due to the presence of a cast function (e.g.,a TIMESTAMP value can be stored inside semi-structured data object ofany TIMESTAMP version, as a NUMBER, as a STRING of TIMESTAMP, as aSTRING of INTEGER).

To extend the pruning index to support predicates on semi-structureddata fields while addressing the foregoing challenges, operations 1105,1110, 1115, 1120, and 1125 can be performed as part of the method 800,as shown in FIG. 11. Consistent with these embodiments, the operation1105 may be performed prior to operation 810 where the compute servicemanager 108 generates the pruning index for the source table.

At operation 1105, the compute service manager 108 generates one or moreindexing transformations for each object in a semi-structured data typecolumn of the source table. The compute service manager 108 can generatean indexing transformation for a given object using a SQL cast function.Invocation of a cast function on an object is also referred to herein as“casting”. The compute service manager 108 uses the cast function toconvert the object to a stored data type. That is, the compute servicemanager 108 can cast the object from an input object type to a storeddata type to generate an indexing transformation. In some instances, thecompute service manager 108 generates an indexing transformation bycasting the object to from a first logical data type (e.g., FIXED) to asecond logical data type (e.g., REAL). In some instances, the computeservice manager 108 generates an indexing transformation by casting theobject to the same logical data type with a different scale and/orprecision (e.g., a FIXED->FIXED (PRECISION, SCALE) transformation).

In instances of ARRAY and OBJECT data types, the compute service manager108 generates an indexing transformation based on a path (e.g., an SQLpath) of the data. More specifically, the compute service manager 108generates a token for the indexing transformation that indicates that acomplex path (corresponding to an ARRAY or OBJECT data types) is notindexed specifically. In an example of the forgoing, input dataincludes:

{“id”: 45, “name”: “John Appleseed”, “age”: 45}

and a received query predicate includes:

src:id=45

In this example, if the path (i.e., “/id/” or “/age/”) is not used whenindexing and matching, the same values will be treated in the same wayand will result in the same hashes. This can be problematic in exampleinstances in which there are sender-receiving IP addresses or the samenumeric values in multiple fields. Even if the “id” was different than45, a pruning index look-up would still identify “45” because it ispresent in the “age” field. To address the challenges illustrated bythis example, a token is generated based on a full absolute path of thedata, as mentioned above.

Table 1, presented below, lists example indexing transformations thatcan be generated for multiple input object data types.

TABLE 1 Input object data type Indexing transformation FIXED FIXED →REAL FIXED → FIXED (PRECISION, SCALE) TEXT REAL REAL REAL → FIXED TEXTTEXT TEXT TEXT → NUMBER (PRECISION, SCALE) DATE TIME TIMESTAMP DATE DATETEXT TIME TIME TEXT TIMESTAMP_ TIMESTAMP_NTZ NTZ TEXT TIMESTAMP_ LTZTIMESTAMP_ TZ BOOLEAN FIXED TEXT NULL_VALUE NULL_VALUE ARRAY PATH OBJECTPATH

With specific reference to FIXED and REAL data types, after parsingnumber input, values can be stored as fixed point (LogicalType::FIXED)or real (LogicalType::REAL) objects. As shown in Table 1, for FIXEDinput data types, the compute service manager 108 applies a FIXED toREAL transformation. The output value of REAL data type is stabilizedand indexed. Additionally, a FIXED to FIXED (precision, scale)transformation is performed, in which the output FIXED data type has adifferent precision or scale (e.g., 0) than the input. Although this mayreduce precision, this enables matching against NUMBER (?,?) casts. AFIXED to TEXT transformation is also performed to enable stringmatching. For REAL input object data types, the REAL value is stabilizedand indexed if it is in the range of FIXED data type. In instances inwhich the value is out-of-range (e.g., 1e+50), the value may bediscarded from indexing. A REAL to FIXED transformation is alsoperformed and out-of-range REAL values are discarded. As with FIXED datatypes, a REAL to TEXT transformation is performed to enable exact stringmatching.

With reference to TEXT data types, textual data is indexed as text forexact string matching. Computed hash values for TEXT and BINARY datatype values are the same, and thus no additional processing is requiredfor BINARY objects. Other data types such as DATE, TIME, and TIMESTAMPcan be stored as TEXT objects. Thus, in order to allow predicates onthese data types, the compute service manager 108 may attempt castingtextual data to each of these data types and keep successful conversionsas indexing transformations.

Consistent with some embodiments, the network-based database system 102can store DATA, TIME, and TIMESTAMP data types in TEXT objects. Theseobject types can, however, be present in semi-structured data typecolumns of tables coming from external scans. When converting validtimestamp strings (and objects) into:

-   -   TIMESTAMP_NTZ=>The output will not contain a timezone (or rather        it is GMT-0). Even if the converted string has its own timezone,        it is discarded.    -   TIMESTAMP_LTZ=>The output will have a local timezone attached.        If the converted string does not have a timezone, the compute        service manager 108 may add the local timezone. If the string        already has a timezone, the compute service manager 108 may        first apply the existing timezone, then attach to the local        timezone.    -   TIMESTAMP_TZ=>There is a source local timezone but it is not        used during computations. If the converted string does not have        a timezone, the compute service manager 108 may add the local        timezone. If the string already has a timezone, that timezone is        used.

Assume STRING=DATE+[TIME]+[TZ] where current local timezone is LTZ.Then:

-   -   STRING->TIMESTAMP_NTZ=>DATE+[TIME]    -   STRING->TIMESTAMP_TZ=>DATE+[TIME]+(TZ=∅?LTZ:TZ)    -   STRING->TIMESTAMP_LTZ=>DATE+[TZ=∅?TIME:TIME+TZ−LTZ]+LTZ        TABLES 2 and 3 presented below provide examples of the foregoing        formula.

TABLE 2 STRING object without timezone information ′2021 Jan. 01 Fri, 01Jan. 2021 23:00:00 + 0000 23:00:00′::variant::timestamp_ntz; ′2021 Jan.01 Fri, 01 Jan. 2021 23:00:00 − 0800 23:00:00′::variant::timestamp_tz;′2021 Jan. 01 Fri, 01 Jan. 2021 23:00:00 − 080023:00:00′::variant::timestamp_ltz;

TABLE 3 STRING object with timezone information ′2021 Jan. 01 23:00:00 −Fri, 01 Jan. 2021 23:00:00 + 0000 1200′::variant::timestamp_ntz; ′2021Jan. 01 23:00:00 − Fri, 01 Jan. 2021 23:00:00 − 12001200′::variant::timestamp_tz; ′2021 Jan. 01 23:00:00 − Sat, 02 Jan. 202103:00:00 − 0800 1200′::variant::timestamp_ltz;

As noted by TABLE 1, indexing transformations for BOOLEAN data typescorrespond to FIXED and TEXT data types. For the BOOLEAN to FIXEDtransformation, the only possible values are ‘0’ and ‘1’. For theBOOLEAN to TEXT transformation, the only possible values are “true” and“false.” The BOOLEAN to FIXED indexing transformation can be especiallyuseful for predicates of the following type:

Consistent with some embodiments, in performing the operation 1105, thecompute service manager 108 may try to cast an object in asemi-structured data type column to one or more stored data types. Thatis, the compute service manager 108 attempts to convert the object tothe stored data type using a SQL cast function. The object can be thefirst object in the column processed by the compute service manager 108.

If the cast fails, the compute service manager 108 stores an indicatorto indicate that objects in the column cannot be cast to that particulardata type. That is, an indicator is stored to indicate that the objectsin the column are unable to be converted to the data type in response toa failed attempt to convert the object to the data type. In an example,the compute service manager 108 can insert a token in the pruning indexthat indicates that objects in the column cannot be cast to a particulardata type. As the compute service manager 108 traverses additionalobjects in the column in generating a filter for the column in thepruning index, the stored token causes the compute service manager 108to avoid further attempts at casting objects in the column to the datatype for which the casting failed. If the cast is successful, thecompute service manager 108 saves a result of the cast as an indexingtransformation for the object and a fingerprint may subsequently begenerated based on the indexing transformation, as described above. Thecompute service manager 108 casts the remaining objects in the column tothe data types for which the cast is successful.

Although only a single data type is addressed above, it shall beappreciated that this process can be repeated for each supported datatype. That is, the compute service manager 108 can try to cast theobject in the column to each of multiple different supported data types.In this manner, the compute service manager 108 can learn which datatypes objects in the column can be cast to based on which data types thefirst object in the column can be cast to, and the compute servicemanager 108 can avoid attempting to cast the remaining objects in thecolumn to data types that they cannot be cast to.

With returned reference to FIG. 11, the operation 1110 can be performedas part of the operation 810 where the compute service manager 108generates the pruning index. At operation 1110, the compute servicemanager 108 generates a set of fingerprints for each data object in thecolumn based on the corresponding indexing transformation(s) generatedfor the data object. That is, the compute service manager 108 generatesa set of fingerprints for a given object based on the one or moreindexing transformations generated for the object. As noted above inreference to operation 905, the set of fingerprints generated for eachobject in the column are used to generate a filter in the pruning indexthat corresponds to the column.

The compute service manager 108 can, in some instances, generate afingerprint for a given object by computing a hash over an indexingtransformation of the object or over the object itself. In other words,the set of fingerprints generated for a given object can include one ormore fingerprints generated by computing a hash over an indexingtransformation and a fingerprint generated by computing a hash over theobject itself.

As discussed above, for complex data types such as ARRAY and OBJECT, theindexing transformation corresponds to a token that indicates the path(corresponding to an ARRAY or OBJECT data types) is not specificallyindexed. In generating a fingerprint for such complex data types, thecompute service manager 108 may compute a first hash over the path, andcompute a second hash over the first hash to produce the fingerprint forthe data. In some example embodiments, the hash function xxHash( ) isused to compute the hashes, though it shall be appreciated that any oneof many known hashing techniques and functions can be used. In a firstexample, the compute service manager 108 can generate a fingerprint fora complex data type as follows:

-   -   XxHash(<constant>, <seed>=<hash_of_the_path>)

In a second example, the compute service manager 108 can generate afingerprint for a complex data type as follows:

-   -   XxHash(XxHash(<constant>, <hash_of_the_path>), PRIME_1)

In a third example, the compute service manager 108 can generate afingerprint for a complex data type as follows:

-   -   XxHash(XxHashCombine(<constant>, <seed>=PRIME_1,        <intermediate>=<hash_of_the_path>), PRIME_1)

As shown, the operations 1115 and 1120 can, in some embodiments, beperformed subsequent to the operation 815 where the query directed tothe source table is received. Consistent with these embodiments, thequery can include a predicate on a semi-structured data type column. Atoperation 1115, the compute service manager 108 generates one or moreindexing transformations based on the query predicate. Similar to theindexing transformations generated for the objects in thesemi-structured column, the compute service manager 108 can generate anindexing transformation for the query predicate by executing a castfunction over one or more values in the predicate. That is, the computeservice manager 108 can use the cast function to convert a value in thequery predicate from a first data type to a second data type or to thesame data type, but with a different precision and/or scale. In someinstances, the value itself can be used as an indexing transformationwithout casting the value to a different data type.

For predicates such as IS NOT NULL(<semi-structured data type field>)the argument semi-structured data field can correspond to both internaland leaf nodes, meaning that there is no information about the existenceof the path. Thus, the compute service manager 108 cannot simply inferwhether this path in fact contains a primitive value or represents anOBJECT or ARRAY data type. Therefore, the compute service manager 108creates an IN predicate as follows:

-   -   (<semi-structured_data_type_field>indexed as LEAF node)

Or

-   -   (<semi-structured_data_type_field>indexed as INTERNAL node)        Accordingly, the compute service manager 108 generates two        constants—one for the leaf path and one for the internal path        hash. These final constants can be fed into the IN predicate.

To address IN predicates in the following form:<semi-structured_data_type_field> in (C1, C2, . . . , Cn), thesemi-structured data type field can be interpreted without any Cast( )functions when the constants C1, C2, . . . , Cn are numbers. Otherwise,the compute service manager 108 can unwrap the Cast for each constantseparately. If the constant to match against is a STRING data type, twoconstants are created.

At operation 1120, the compute service manager 108 generates a set ofsearch fingerprints based on the one or more indexing transformations.As with the fingerprints generated for the searchable values in thesemi-structured column, the compute service manager 108 generates afingerprint for the query predicate by computing a hash over an indexingtransformation generated for the query predicate.

As shown, the operation 1125 can be performed as part of the operation825 where the compute service manager 108 prunes the scan set. Atoperation 1125, the compute service manager 108 identifies a subset ofmicro-partitions to scan based on the pruning index and the searchfingerprints. The compute service manager 108 can identify the subset bycomparing the set of search fingerprints to values included in thepruning index (e.g., fingerprints of indexing transformations of storeddata values in the source table), and identifying one or more values inthe pruning index that match one or more search fingerprints.Specifically, the compute service manager 108 identifies one or moremicro-partitions that potentially store data that satisfies the querybased on fingerprints in the pruning index that match searchfingerprint(s). That is, a fingerprint (e.g., hash value computed basedon a indexing transformations of semi-structured data value) in thepruning index that matches a search fingerprint generated from aindexing transformation of the query predicate (e.g., a hash valuecomputed based on the indexing transformation) indicates that matchingdata is potentially stored in a corresponding column of themicro-partition because the indexing transformation generated from thequery predicate is stored in the column of the micro-partition. Thecorresponding micro-partition can be identified by the compute servicemanager 108 based on the matching fingerprint in the pruning index.

As shown in FIG. 12, the method 800 may, in some embodiments, includeoperations 1205, 1210, 1215, 1220, and 1225. Consistent with theseembodiments, the operations 1205 and 1210 may be performed prior tooperation 810 where the compute service manager 108 generates thepruning index for the source table. At operation 1205, the computeservice manager 108 preprocesses the data values in the cells of thesource table. In preprocessing a given data value, the compute servicemanager 108 generates one or more preprocessed variants of the datavalue. In performing the preprocessing, the compute service managerperforms one or more normalization operations to a given data value. Thecompute service manager 108 can utilize one of several knownnormalization techniques to normalize data values (e.g., normalizationform canonical composition).

For a given data value, the preprocessing performed by the computeservice manager 108 can include, for example, any one or more of:generating a case-agnostic variant (e.g., by converting uppercasecharacters to lowercase characters), generating one or more misspelledvariants based on common or acceptable misspellings of the data value,and generating one or more synonymous variants corresponding to synonymsof the data value. In general, in generating a preprocessed variant(e.g., case-agnostic variant, misspelled variant, a synonymous variantor a variant with special characters to indicate a start and end to adata value), the compute service manager 108 uses a common knowledgebase to transform a data value into one or more permutations of theoriginal data value.

As an example of the foregoing, the string “Bob” can be transformed intoto the case-agnostic variant “bob.” As another example, the preprocessedvariants of “bob” “bbo” and “obb” can be generated for the string “Bob”to account for misspellings.

At operation 1210, the compute service manager 108 generates a set ofN-grams for each preprocessed variant. An N-gram in this context refersto a contiguous sequence of N-items (e.g., characters or words) in agiven value. For a given preprocessed variant of a data value in thesource table, the compute service manager 108 transforms the value intomultiple segments of equal length. For example, for a string, thecompute service manager 108 can transform the string into multiplesub-strings of N characters.

Depending on the embodiment, the value of N can be predetermined ordynamically computed at the time of generating the pruning index. Inembodiments in which the value of N is precomputed, the compute servicemanager 108 determines an optimal value for N based on a data type ofvalues in the source table. In some embodiments, multiple values of Ncan be used. That is, a first subset of N-grams can be generated using afirst value for N and a second subset of N-grams can be created using asecond value of N.

Consistent with these embodiments, the operations 1215 and 1220 can beperformed prior to operation 820 where the compute service manager 108prunes the scan set using the pruning index. At operation 1215, thecompute service manager 108 preprocesses a search pattern included inthe query. In preprocessing the search pattern, the compute servicemanager 108 performs the same preprocessing operations that areperformed on the data values in the source table at 1205 to ensure thatthe characters of the search pattern fit the pruning index. Hence, inpreprocessing the search pattern, the compute service manager 108 canperform any one or more of: generating a case-agnostic variant of thesearch pattern (e.g., by converting uppercase characters to lowercasecharacters), generating one or more misspelled variants based on commonor acceptable misspellings of the search pattern, generating one or moresynonymous variants corresponding to synonyms of the search pattern, andgenerating a variant that includes special characters to mark a startand end of the search pattern. In preprocessing a given pattern, thecompute service manager 108 can generate one or more preprocessedvariants of the search pattern. For example, the compute service manager108 can generate any one or more of: a case-agnostic variant, misspelledvariant, or a synonymous variant for the search pattern. As a furtherexample, the compute service manager 108 can generate a variant thatincludes special characters to indicate a start and end of a searchpattern (e.g., “{circumflex over ( )}testvalue$” for the search pattern“testvalue”).

At operation 1220, the compute service manager 108 generates a set ofN-grams for the search pattern based on the one or more preprocessedvariants of the search pattern. The compute service manager 108 uses thesame value for N that was used to generate the pruning index. Inembodiments in which the compute service manager 108 uses multiplevalues for N in generating the pruning index, the compute servicemanager 108 uses the same values for generating the set of N-grams forthe search pattern.

In an example, the query includes the following statement:

WHERE a ILIKE ‘% LoremIpsum % Dolor % Sit % Ame’

In this example, ‘% LoremIpsum % Dolor % Sit % Amet’ is the searchpattern, and in preprocessing the search pattern, the compute servicemanager 108 converts the search pattern to all lower case to create acase-agnostic variant: ‘% loremipsum % dolor % sit % amet’. The computeservice manager 108 splits the search pattern into segments at the wildcard positions, which, in this example, produces the followingsub-strings: “loremipsum”, “dolor”, “sit”, and “amet”. Based on thesesub-strings, the compute service manager 108 generates the following setof N-grams:

-   -   Set [“lorem”, “oremi”, “remip”, “emips”, “mipsu”, “ipsum”,        “dolor”]        In this example N is 5, and thus the compute service manager 108        discards the sub-strings “sit” and “amet” as their length is        less than 5.

As shown, consistent with these embodiments, the operation 1225 can beperformed as part of the operation 825 where the compute service manager108 prunes the scan set using the pruning index. At operation 1225, thecompute service manager 108 uses the set of N-grams generated based onthe search pattern to identify a subset of micro-portions of the sourcetable to scan based on the pruning index. The compute service manager108 may identify the subset of micro-partitions by generating a set offingerprints based on the set of N-grams (e.g., by computing a hash foreach N-gram), comparing the set of fingerprints to values included inthe pruning index (e.g., fingerprints of stored data values in thesource table), and identifying one or more values in the pruning indexthat match one or more fingerprints in the set of fingerprints generatedbased on the N-grams of the search pattern. Specifically, the computeservice manager 108 identifies one or more micro-partitions thatpotentially store data that satisfies the query based on fingerprints ofdata values in the pruning index that match fingerprints in the set offingerprints computed for the search pattern. That is, a fingerprint(e.g., hash value computed based on an N-gram of a preprocessed storeddata value in the source table) in the pruning index that matches afingerprint generated from an N-gram of the search pattern (e.g., a hashvalue computed based on the N-gram) indicates that matching data ispotentially stored in a corresponding column of the micro-partitionbecause the N-gram generated from the search pattern is stored in thecolumn of the micro-partition. The corresponding micro-partition can beidentified by the compute service manager 108 based on the matchingfingerprint in the pruning index. Consistent with some embodiments, inidentifying the subset of micro-partitions, the compute service manager108 uses the pruning index to identify any micro-partitions that containany one of the fingerprints generated from the search pattern N-grams,and from these micro-partitions, the compute service manager 108 thenidentifies the micro-partitions that contain all of the N-grams. Thatis, the compute service manager 108 uses the pruning index to identify asubset of micro-partitions that contain data matching all fingerprintsgenerated based on the N-grams of the search pattern. For example, givenfingerprints f1, f2, and f3, the compute service manager 108 uses thepruning index to determine: a first micro-partition and secondmicro-partition contain data corresponding to f1; the secondmicro-partition and a third micro-partition that contains datacorresponding to f2; and the first, second, and third micro-partitioncontain data corresponding to f3. In this example, the compute servicemanager 108 selects only the second micro-partition for scanning basedon the second micro-partition containing data that corresponds to allthree fingerprints.

FIG. 13 is a flow diagram illustrating operations of the network-baseddatabase system 102 in performing a method 1300 for processing a joinquery using a pruning index, in accordance with some embodiments. Themethod 1300 may be embodied in computer-readable instructions forexecution by one or more hardware components (e.g., one or moreprocessors) such that the operations of the method 1300 may be performedby components of database system 102. Accordingly, the method 1300 isdescribed below, by way of example with reference thereto. However, itshall be appreciated that the method 1300 may be deployed on variousother hardware configurations and is not intended to be limited todeployment within the database system 102.

Depending on the embodiment, an operation of the method 1300 may berepeated in different ways or involve intervening operations not shown.Though the operations of the method 1300 may be depicted and describedin a certain order, the order in which the operations are performed mayvary among embodiments, including performing certain operations inparallel or performing sets of operations in separate processes.

At operation 1305, the compute service manager 108 receives a join queryto combine rows from a first and second table. An example join query isaddressed above in reference to method 800.

At runtime, the execution platform 110 performs a join build phase(operation 1310) in response to receiving the join query. During thejoin build phase, the execution platform 110 builds a hash table whererows from the build side table (e.g., the smaller of the two tables) arestored using the join attribute(s) as a hash key. In building the hashtable, the execution platform 110 collects a set of build side values(e.g., the values to be matched against the probe side table) for thejoin predicate.

At operation 1315, the execution platform 110 evaluates whether apruning index for the probe side table (e.g., the larger of the twotables) exists. If so, the method 1300 advances to operation 1320 wherethe execution platform 110 determines whether to use the pruning indexfor pruning in processing the query. In doing so, the execution platform110 uses a cost function to evaluate the use of the pruning index. Ifthe cost associated with using the pruning index is too high (e.g., thecost exceeds a predetermined cost threshold), the execution platform 110does not use the pruning index. Otherwise, the execution platform 110uses the pruning index to prune the set of micro-partitions to scan fordata matching the query.

As shown, in some embodiments, the evaluation performed at operation1320 can include operations 1321 and 1322. At operation 1321, theexecution platform 110 determines whether a number of values in buildside values satisfies a threshold constraint. In determining whether thenumber of values satisfies the threshold constraint, the executionplatform 110 may compare the number of values in the set to a thresholdnumber, and if the number of values in the set does not exceed thethreshold number, the execution platform 110 determines the number ofvalues in the set satisfies the threshold constraint. If the number ofvalues in the set satisfies the threshold constraint, the method 1300advances to operation 1322 where the execution platform 110 determineswhether the number of micro-partitions in the probe side table satisfiesa threshold constraint. In determining whether the number ofmicro-partitions satisfies the threshold constraint, the executionplatform 110 may compare the number of micro-partitions in the probeside table to a threshold number of micro-partitions, and if the numberof micro-partitions does not exceed the threshold number, the executionplatform 110 determines the number of micro-partitions in the setsatisfies the threshold constraint.

If the execution platform 110 decides to use the pruning index based onthe cost function (e.g., if the number of build side values satisfiesthe threshold constraint and if the number of micro-partitions in thetable satisfies the threshold constraint), the execution platform 110generates an index access plan based on the set of build side values, atoperation 1325. The index access plan identifies the pruning index andspecifies the set of build side values to compare against the pruningindex to determine the reduced scan set. In some embodiments, theexecution platform 110 may, as part of generating the access plan,generate a data structure based on the set of values. As a non-limitingexample, the data structure can be a list of the values.

Based on the index access plan, the execution platform 110 prunes a setof micro-partitions of the probe side table using the pruning index, atoperation 1330. That is, the execution platform 110 uses the pruningindex to identify a subset of micro-partitions of the probe side tableto scan based on the index access plan. For example, the executionplatform 110 may generate one or more fingerprints for each value in theset of values (e.g., by computing a hash over the values) and comparethe computed fingerprints to the pruning index to identify a reducedscan set comprising only a subset of the micro-partitions of the probeside table in which data that satisfies the query is potentially stored.

At operation 1335, the execution platform 110 performs a probe phaseusing only the subset of micro-partitions. During the probe phase, oneor more execution nodes of the execution platform 110 read rows from thereduced scan set of the probe side table and probes the hash table formatching rows using a join attribute as the lookup key. For each matchthat is identified, a joined row is returned.

As shown, if the pruning index for the probe side table does not existor if the execution platform 110 decides not to use the pruning indexbased on the cost function (e.g., if the number of values in the set ofbuild side values does not satisfy the constraint, and/or if the numberof micro-partitions in the probe side table does not satisfy theconstraint), the method 1300 moves to operation 1340 where the executionplatform 110 uses an alternative pruning methodology such as joinpruning.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1 is a database system comprising: at least one hardwareprocessor; and at least one memory storing instructions that cause theat least one hardware processor to perform operations comprising:receiving a query directed at a table organized into a set of batchunits, the query comprising a predicate for which values are unknownprior to runtime; determining a set of values for the predicate based onthe query; creating an index access plan based on the set of values;based on the index access plan, pruning the set of batch units using apruning index associated with the table, the pruning index comprising aset of filters that index distinct values in each column of the table,the pruning of the set of batch units comprising identifying a subset ofbatch units to scan for data that satisfies the query; and scanning thesubset of batch units of the table to identify data that satisfies thequery.

Example 2 includes the system of Example 1, wherein the operationsfurther comprise: evaluating whether to use the pruning index in pruningthe set of batch units based on a cost function.

Example 3 includes the system of any one or more of Examples 1 or 2,wherein the evaluating whether to use the pruning index in pruning theset of batch units comprises: determining whether the set of valuessatisfy a threshold size constraint, wherein the pruning of the set ofbatch units using the pruning index is based on determining the set ofvalues satisfy the threshold size constraint.

Example 4 includes the system of any one or more of Examples 1-3,wherein the evaluating whether to use the pruning index in pruning theset of batch units comprises: determining whether the set of batch unitssatisfy a threshold size constraint, wherein the pruning of the set ofbatch units using the pruning index is based on determining the set ofbatch units satisfy the threshold size constraint.

Example 5 includes the system of any one or more of Examples 1-4,wherein the creating of the index access plan comprises generating adata structure based on the set of values.

Example 6 includes the system of any one or more of Examples 1-5,wherein: the table is a first table; the query comprises a join clauseto combine rows from the first table and a second table; determining theset of values comprises collecting build-side values based on performinga build phase in response to the join clause being included in thequery; and the scanning of the subset of batch units is performed aspart of a join probe phase.

Example 7 includes the system of any one or more of Examples 1-6,wherein the determining of the set of values for the predicate comprisesexecuting a subquery within the query.

Example 8 includes the system of any one or more of Examples 1-7,wherein the operations further comprise: generating the pruning indexbased on the table in an offline process prior to receiving the query.

Example 9 includes the system of any one or more of Examples 1-8,wherein: generating the pruning index comprises generating a filter foreach batch unit of the set of batch units in the table; and thegenerating of the filter for each batch unit comprises generating afirst filter for a first batch unit by performing operations comprising:for a given data value in the first batch unit, generating at least onefingerprint for the data value; and populating the first filter usingthe at least one fingerprint for the data value.

Example 10 includes the system of any one or more of Examples 1-9,wherein: determining the set of values for the predicate is performed atruntime; and pruning the set of batch units is performed at runtime.

Example 11 is a method comprising: receiving a query directed at a tableorganized into a set of batch units, the query comprising a predicatefor which values are unknown prior to runtime; determining a set ofvalues for the predicate based on the query; creating an index accessplan based on the set of values; based on the index access plan, pruningthe set of batch units using a pruning index associated with the table,the pruning index comprising a set of filters that index distinct valuesin each column of the table, the pruning of the set of batch unitscomprising identifying a subset of batch units to scan for data thatsatisfies the query; and scanning the subset of batch units of the tableto identify data that satisfies the query.

Example 12 includes the method of Example 11, wherein the operationsfurther comprise: evaluating whether to use the pruning index in pruningthe set of batch units based on a cost function.

Example 13 includes the method of any one or more of Examples 11 or 12,wherein the evaluating whether to use the pruning index in pruning theset of batch units comprises: determining whether the set of valuessatisfy a threshold size constraint, wherein the pruning of the set ofbatch units using the pruning index is based on determining the set ofvalues satisfy the threshold size constraint.

Example 14 includes the method of any one or more of Examples 11-13,wherein the evaluating whether to use the pruning index in pruning theset of batch units comprises: determining whether the set of batch unitssatisfy a threshold size constraint, wherein the pruning of the set ofbatch units using the pruning index is based on determining the set ofbatch units satisfy the threshold size constraint.

Example 15 includes the method of any one or more of Examples 11-14,wherein the creating of the index access plan comprises generating adata structure based on the set of values.

Example 16 includes the method of any one or more of Examples 11-15,wherein: the table is a first table; the query comprises a join clauseto combine rows from the first table and a second table; determining theset of values comprises collecting build-side values based on performinga build phase in response to the join clause being included in thequery; and the scanning of the subset of batch units is performed aspart of a join probe phase.

Example 17 includes the method of any one or more of Examples 11-16,wherein the determining of the set of values for the predicate comprisesexecuting a subquery within the query.

Example 18 includes the method of any one or more of Examples 11-17, andfurther includes: generating the pruning index based on the table in anoffline process prior to receiving the query.

Example 19 includes the method of any one or more of Examples 11-18,wherein: generating the pruning index comprises generating a filter foreach batch unit of the set of batch units in the table; and thegenerating of the filter for each batch unit comprises generating afirst filter for a first batch unit by performing operations comprising:for a given data value in the first batch unit, generating at least onefingerprint for the data value; and populating the first filter usingthe at least one fingerprint for the data value.

Example 20 includes the method of any one or more of Examples 11-19,wherein: determining the set of values for the predicate is performed atruntime; and pruning the set of batch units is performed at runtime.

Example 21 is a computer-storage medium storing instructions that causeat least one hardware processor to perform operations comprising:receiving a query directed at a table organized into a set of batchunits, the query comprising a predicate for which values are unknownprior to runtime; determining, at runtime, a set of values for thepredicate based on the query; creating an index access plan based on theset of values; based on the index access plan, pruning, at runtime, theset of batch units using a pruning index associated with the table, thepruning index comprising a set of filters that index distinct values ineach column of the table, the pruning of the set of batch unitscomprising identifying a subset of batch units to scan for data thatsatisfies the query; and scanning the subset of batch units of the tableto identify data that satisfies the query.

Example 22 includes the computer-storage medium of Example 21, whereinthe operations further comprise: evaluating whether to use the pruningindex in pruning the set of batch units based on a cost function.

Example 23 includes the computer-storage medium of any one or more ofExamples 21 or 22, wherein the evaluating whether to use the pruningindex in pruning the set of batch units comprises: determining whetherthe set of values satisfy a threshold size constraint, wherein thepruning of the set of batch units using the pruning index is based ondetermining the set of values satisfy the threshold size constraint.

Example 24 includes the computer-storage medium of any one or more ofExamples 21-23, wherein the evaluating whether to use the pruning indexin pruning the set of batch units comprises: determining whether the setof batch units satisfy a threshold size constraint, wherein the pruningof the set of batch units using the pruning index is based ondetermining the set of batch units satisfy the threshold sizeconstraint.

Example 25 includes the computer-storage medium of any one or more ofExamples 21-24, wherein the creating of the index access plan comprisesgenerating a data structure based on the set of values.

Example 26 includes the computer-storage medium of any one or more ofExamples 21-25, wherein: the table is a first table; the query comprisesa join clause to combine rows from the first table and a second table;determining the set of values comprises collecting build-side valuesbased on performing a build phase in response to the join clause beingincluded in the query; and the scanning of the subset of batch units isperformed as part of a join probe phase.

Example 27 includes the computer-storage medium of any one or more ofExamples 21-26, wherein the determining of the set of values for thepredicate comprises executing a subquery within the query.

Example 28 includes the computer-storage medium of any one or more ofExamples 21-27, wherein the operations further comprise: generating thepruning index based on the table in an offline process prior toreceiving the query.

Example 29 includes the computer-storage medium of any one or more ofExamples 21-28, wherein: generating the pruning index comprisesgenerating a filter for each batch unit of the set of batch units in thetable; and the generating of the filter for each batch unit comprisesgenerating a first filter for a first batch unit by performingoperations comprising: for a given data value in the first batch unit,generating at least one fingerprint for the data value; and populatingthe first filter using the at least one fingerprint for the data value.

Example 30 includes the computer-storage medium of any one or more ofExamples 21-29, wherein: determining the set of values for the predicateis performed at runtime; and pruning the set of batch units is performedat runtime.

FIG. 14 illustrates a diagrammatic representation of a machine 1400 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1400 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 14 shows a diagrammatic representation of the machine1400 in the example form of a computer system, within which instructions1416 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1400 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1416 may cause the machine 1400 to execute anyone or more operations of any one or more of the methods 800 or 1300. Asanother example, the instructions 1416 may cause the machine 1400 toimplement portions of the functionality illustrated in any one or moreof FIGS. 4-8. In this way, the instructions 1416 transform a general,non-programmed machine into a particular machine 1400 (e.g., the computeservice manager 108, the execution platform 110, and the data storagedevices 206) that is specially configured to carry out any one of thedescribed and illustrated functions in the manner described herein.

In alternative embodiments, the machine 1400 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1400 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1400 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1416, sequentially orotherwise, that specify actions to be taken by the machine 1400.Further, while only a single machine 1400 is illustrated, the term“machine” shall also be taken to include a collection of machines 1400that individually or jointly execute the instructions 1416 to performany one or more of the methodologies discussed herein.

The machine 1400 includes processors 1410, memory 1430, and input/output(I/O) components 1450 configured to communicate with each other such asvia a bus 1402. In an example embodiment, the processors 1410 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1412 and aprocessor 1414 that may execute the instructions 1416. The term“processor” is intended to include multi-core processors 1410 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1416 contemporaneously. AlthoughFIG. 14 shows multiple processors 1410, the machine 1400 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1430 may include a main memory 1432, a static memory 1434,and a storage unit 1436, all accessible to the processors 1410 such asvia the bus 1402. The main memory 1432, the static memory 1434, and thestorage unit 1436 store the instructions 1416 embodying any one or moreof the methodologies or functions described herein. The instructions1416 may also reside, completely or partially, within the main memory1432, within the static memory 1434, within the storage unit 1436,within at least one of the processors 1410 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1400.

The I/O components 1450 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1450 thatare included in a particular machine 1400 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1450 mayinclude many other components that are not shown in FIG. 14. The I/Ocomponents 1450 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1450 mayinclude output components 1452 and input components 1454. The outputcomponents 1452 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1454 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1450 may include communication components 1464operable to couple the machine 1400 to a network 1480 or devices 1470via a coupling 1482 and a coupling 1472, respectively. For example, thecommunication components 1464 may include a network interface componentor another suitable device to interface with the network 1480. Infurther examples, the communication components 1464 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1470 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1400 may correspond to any one ofthe compute service manager 108, the execution platform 110, and thedevices 1470 may include the data storage device 206 or any othercomputing device described herein as being in communication with thenetwork-based database system 102 or the storage platform 104.

The various memories (e.g., 1430, 1432, 1434, and/or memory of theprocessor(s) 1410 and/or the storage unit 1436) may store one or moresets of instructions 1416 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1416, when executed by theprocessor(s) 1410, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1480may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1480 or a portion of the network1480 may include a wireless or cellular network, and the coupling 1482may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1482 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1416 may be transmitted or received over the network1480 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1464) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1416 may be transmitted or received using a transmission medium via thecoupling 1472 (e.g., a peer-to-peer coupling) to the devices 1470. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1416 for execution by the machine 1400, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the method 800 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardwareprocessor; and at least one memory storing instructions that cause theat least one hardware processor to perform operations comprising:receiving a query comprising a join clause to combine rows from a firsttable and a second table, the first table being organized into a set ofbatch units; collecting a set of build-side values from the second tablebased on performing a build phase in response to the join clause beingincluded in the query; creating an index access plan based on the set ofbuild-side values; based on the index access plan, pruning the set ofbatch units using a pruning index associated with the first table, thepruning index comprising a set of filters that index distinct values ineach column of the first table, the pruning of the set of batch unitscomprising identifying a subset of batch units to scan for data thatsatisfies the query; and performing a probe phase, the performing of theprobe phase comprising scanning the subset of batch units of the firsttable to identify data that satisfies the query.
 2. The system of claim1, wherein the operations further comprise: evaluating whether to usethe pruning index in pruning the set of batch units based on a costfunction.
 3. The system of claim 2, wherein the evaluating whether touse the pruning index in pruning the set of batch units comprises:determining whether the set of build-side values satisfy a thresholdsize constraint, wherein the pruning of the set of batch units using thepruning index is based on determining the set of build-side valuessatisfy the threshold size constraint.
 4. The system of claim 2, whereinthe evaluating whether to use the pruning index in pruning the set ofbatch units comprises: determining whether the set of batch unitssatisfy a threshold size constraint, wherein the pruning of the set ofbatch units using the pruning index is based on determining the set ofbatch units satisfy the threshold size constraint.
 5. The system ofclaim 1, wherein the creating of the index access plan comprisesgenerating a data structure based on the set of build-side values. 6.The system of claim 5, wherein the data structure comprises a list ofone or more build-side values.
 7. The system of claim 1, wherein theoperations further comprise: generating the pruning index based on thetable in an offline process prior to receiving the query.
 8. The systemof claim 7, wherein: generating the pruning index comprises generating afilter for each batch unit of the set of batch units in the table; andthe generating of the filter for each batch unit comprises generating afirst filter for a first batch unit by performing operations comprising:for a given data value in the first batch unit, generating at least onefingerprint for the data value; and populating the first filter usingthe at least one fingerprint for the data value.
 9. The system of claim1, wherein performing the build phase comprises building a hash tablebased on the set of build-side values using a join attribute as a hashkey.
 10. The system of claim 9, wherein performing the probe phasecomprises: reading a row from the subset of batch units of the firsttable; and probing the hash table for a matching row using the joinattribute as a lookup key.
 11. A method comprising: receiving a querycomprising a join clause to combine rows from a first table and a secondtable, the first table being organized into a set of batch units;collecting a set of build-side values from the second table based onperforming a build phase in response to the join clause being includedin the query; creating an index access plan based on the set ofbuild-side values; based on the index access plan, pruning, by at leastone hardware processor, the set of batch units using a pruning indexassociated with the first table, the pruning index comprising a set offilters that index distinct values in each column of the first table,the pruning of the set of batch units comprising identifying a subset ofbatch units to scan for data that satisfies the query; and performing aprobe phase, the performing of the probe phase comprising scanning thesubset of batch units of the first table to identify data that satisfiesthe query.
 12. The method of claim 11, further comprising: evaluatingwhether to use the pruning index in pruning the set of batch units basedon a cost function.
 13. The method of claim 12, wherein the evaluatingwhether to use the pruning index in pruning the set of batch unitscomprises: determining whether the set of build-side values satisfy athreshold size constraint, wherein the pruning of the set of batch unitsusing the pruning index is based on determining the set of build-sidevalues satisfy the threshold size constraint.
 14. The method of claim12, wherein the evaluating whether to use the pruning index in pruningthe set of batch units comprises: determining whether the set of batchunits satisfy a threshold size constraint, wherein the pruning of theset of batch units using the pruning index is based on determining theset of batch units satisfy the threshold size constraint.
 15. The methodof claim 11, wherein the creating of the index access plan comprisesgenerating a data structure based on the set of build-side values. 16.The method of claim 15, wherein the data structure comprises a list ofone or more build-side values.
 17. The method of claim 11, furthercomprising: generating the pruning index based on the table in anoffline process prior to receiving the query.
 18. The method of claim17, wherein: generating the pruning index comprises generating a filterfor each batch unit of the set of batch units in the table; and thegenerating of the filter for each batch unit comprises generating afirst filter for a first batch unit by performing operations comprising:for a given data value in the first batch unit, generating at least onefingerprint for the data value; and populating the first filter usingthe at least one fingerprint for the data value.
 19. The method of claim11, wherein performing the build phase comprises building a hash tablebased on the set of build-side values using a join attribute as a hashkey.
 20. The method of claim 19, wherein performing the probe phasecomprises: reading a row from the subset of batch units of the firsttable; and probing the hash table for a matching row using the joinattribute as a lookup key.
 21. A computer-storage medium comprisinginstructions that, when executed by one or more processors of a machine,configure the machine to perform operations comprising: receiving aquery comprising a join clause to combine rows from a first table and asecond table, the first table being organized into a set of batch units;collecting a set of build-side values from the second table based onperforming a build phase in response to the join clause being includedin the query; creating an index access plan based on the set ofbuild-side values; based on the index access plan, pruning the set ofbatch units using a pruning index associated with the first table, thepruning index comprising a set of filters that index distinct values ineach column of the first table, the pruning of the set of batch unitscomprising identifying a subset of batch units to scan for data thatsatisfies the query; and performing a probe phase, the performing of theprobe phase comprising scanning the subset of batch units of the firsttable to identify data that satisfies the query.
 22. Thecomputer-storage medium of claim 21, wherein the operations furthercomprise: evaluating whether to use the pruning index in pruning the setof batch units based on a cost function.
 23. The computer-storage mediumof claim 22, wherein the evaluating whether to use the pruning index inpruning the set of batch units comprises: determining whether the set ofbuild-side values satisfy a threshold size constraint, wherein thepruning of the set of batch units using the pruning index is based ondetermining the set of build-side values satisfy the threshold sizeconstraint.
 24. The computer-storage medium of claim 22, wherein theevaluating whether to use the pruning index in pruning the set of batchunits comprises: determining whether the set of batch units satisfy athreshold size constraint, wherein the pruning of the set of batch unitsusing the pruning index is based on determining the set of batch unitssatisfy the threshold size constraint.
 25. The computer-storage mediumof claim 21, wherein the creating of the index access plan comprisesgenerating a data structure based on the set of build-side values. 26.The computer-storage medium of claim 25, wherein the data structurecomprises a list of one or more build-side values.
 27. Thecomputer-storage medium of claim 21, wherein the operations furthercomprise: generating the pruning index based on the table in an offlineprocess prior to receiving the query.
 28. The computer-storage medium ofclaim 27, wherein: generating the pruning index comprises generating afilter for each batch unit of the set of batch units in the table; andthe generating of the filter for each batch unit comprises generating afirst filter for a first batch unit by performing operations comprising:for a given data value in the first batch unit, generating at least onefingerprint for the data value; and populating the first filter usingthe at least one fingerprint for the data value.
 29. Thecomputer-storage medium of claim 21, wherein performing the build phasecomprises building a hash table based on the set of build-side valuesusing a join attribute as a hash key.
 30. The computer-storage medium ofclaim 29, wherein performing the probe phase comprises: reading a rowfrom the subset of batch units of the first table; and probing the hashtable for a matching row using the join attribute as a lookup key.